1

Overhead Conductor Condition Monitoring

ASTP/API Quarterly Progress Report (No.1 2019)

Prepared by:

Dr Lakshitha Naranpanawe, Dr Hui Ma and Professor Tapan Saha

Power & Energy Systems Research Group

School of Information Technology & Electrical Engineering

The University of Queensland

Brisbane, Queensland, May 2019

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Overhead Conductor Condition Monitoring

Quarterly Progress Report (No. 1 2019)

Produced by

Dr Lakshitha Naranpanawe, Dr Hui Ma and Professor Tapan Saha

This report was prepared as part of the “Overhead Conductor Condition Monitoring” project being funded by

the Energy Networks Association Limited.

To be cited as “Overhead Conductor Condition Monitoring (Quarterly Progress Report No 1 of 2019)”, The

University of Queensland, St. Lucia, 2018.

Power & Energy Systems Research Group

Axon building (#47), Staff House Road, St. Lucia Campus

The University of Queensland

Brisbane, QLD 4072 Australia

May 6 2019

Checked by Project Industry Partners:

Issue Changes Date

1 New document May 6, 2019

Signature Date

Ayan Ghosal (Western Power)

Matthew Cupples (AusGrid)

Peter Livingston (CitiPower/Powercor/United Energy)

Tony Stevens (SA Power)

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Executive Summary

Energy Networks Association (ENA) member utilities have almost 800,000 kilometres of

overhead conductor in service, valued at several billion dollars. Many of this critical

infrastructure is ageing with some already reaching 70 years. This project investigates the

effective ways of condition monitoring of overhead conductor in Australian distribution

networks. The objectives are:

Review of conductor failure modes, degradation mechanisms and ageing parameters and

current Australian industry practices to asset manage overhead conductors.

Define the criteria for quantifying conductor condition and its end-of-life, and determine

the probability of conductor failure and estimate its remaining useful life.

Identify the core areas of research and development for improving condition assessment of

conductors in Australian Distribution Network Service Providers (DNSPs) networks.

Survey state-of-the-art conductor condition monitoring techniques that could be used to

monitor distribution conductor condition and assess the practicality and economics of

applying these techniques in Australian networks.

This project was started on June 20, 2018. Having been working closely with project industry

partners, the UQ team has successfully completed Milestone 1 tasks, including:

A comprehensive review on the types, failures modes and geographical locales of

conductors in Australian DNSPs’ networks. The review was based on an extensive

literature study on ENA 2015-2016 conductor survey and individual utility surveys, open

source databases, IEEE, IEC and CIGRE standards and recommendations and other

literature as well as discussions with industry experts.

A comprehensive study to understand the conductor degradation mechanism and

parameters that affect each type of degradation mechanism in Australian DNSPs’ networks.

After a survey of the current Australian DNSPs’ practice on conductor asset management

and their requirements for a proactive yet cost-effective conductor condition monitoring,

the UQ team identified core areas of research and development for an improved condition

assessment of conductors in Australian DNSPs’ circuits.

The Milestone 1 report was submitted to ENA on December 20, 2018. Since the completion of

Milestone 1, the UQ team has conducted a feasibility study on the implementation of conductor

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health index methodology in Australian DNSP’s networks. In the past four months (from

January 1 to April 30 2019), the UQ team made the following progress:

Conducted a feasibility study on the application of health index method to improve the

condition assessment of bare overhead conductors in Australian distribution networks.

After carefully reviewing two representative health index methods i.e. Canadian method

and EA technology method, the UQ team found that the Canadian method may not be

suitable for Australian distribution networks while the parameters and their weightages

used in the EA technology method need to be further investigated.

Developed a model using corrosion induced conductor failure data. The model can provide

an estimation on the critical age, at which ACSR conductors in different geographical zones

may fail due to corrosion.

Developed a tool to estimate the remaining life of overhead conductors. The tool used a set

of indices to determine the conductor age de-rating factors, including operating conditions,

geographical locations, frequencies of fault occurrences, the number of mid-span joints and

the repair history.

Several Australian DNSPs have been using commercial software tools for condition-based

asset remaining life estimation. However, several issues are still not clear which impair the

applicability of these tools, especially (1) what parameters are the most suitable as the inputs

to the tools; (2) how to determine the weights to these parameters; and (3) how to obtain these

parameters from in-service circuits. Within the period of Millstone 2, the UQ team will focus

on the above three issues. In addition, the UQ team is planning to start the feasibility study of

using drone to take high-resolution photos of conductors and then evaluate the conductor

condition from these photos. This is the first task in Milestone 3 of the project proposal.

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Table of Contents

1. Introduction 6

1.1 Background 6

1.2 Highlight of Project Progress 6

2. Industry and University Partners – Current 8

2.1 University team: 8

2.2 Industry: 8

3. Project Progress / Methodology 9

3.1 A Brief Review of Canadian Methodology of Conductor Health Index Calculation 10

3.2 A Brief Review of EA Technology’s CBRM Methodology of Conductor Health Index Calculation 11

3.3 A Brief Introduction of the UQ Team’s Methodology of Estimating Remaining Life of Conductors 13

3.4 Further Discussions on the Canadian and EA Technology Methods of Calculating Conductor Health Index 14

3.5 Input Parameters Proposed as Inputs for Calculating Conductor Health Index 15

3.6 Conductor Failure Data of Australian Distribution Networks 16

3.7 Weibull Analysis for Extracting Conductor Life Information Data 18

4. Future Works 20

5. Conclusions and Recommendations 20

6. References 21

7. Appendix 22

7.1 Risk Matrix 22

7.2 Gantt Chart/ Project Plan 23

7.3 Financial statement 24

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1. Introduction

1.1 Background

ENA members have almost 800,000 circuit kilometres of overhead conductor in service. This

represents an asset, which is conservatively valued over several billion dollars. Overhead

conductor asset can be of different metal types, different sizes and capacities, and is employed

in all climatic zones including tropical, temperate, arid, and highlands. Many conductor assets

are ageing with some already reaching 70 years. Though many advancements in technology

have been achieved throughout these years, approaches to cost-effectively monitor condition

of bulk of conductors have not substantially changed. Existing conductor condition monitoring

practices are still visual inspections and conductor replacement is usually driven by the

frequency of conductor failures [1]. Reliable and cost-effective methods to assess the likelihood

of a conductor failure have not yet been developed for the Australian distribution network

service providers (DNSP) networks.

On June 20th 2018, ENA approved a research project proposal submitted by the University of

Queensland team. The project is aimed at investigating how to effectively monitor and assess

the condition of overhead conductor for an improved asset management of conductors in

Australian distribution networks. The research works conducted in this project are:

1. An establishment of a knowledge base of overhead conductors in Australian distribution

networks and subsequently the identification of the current core areas of research and

development for an improved conductor condition assessment;

2. A methodology for quantifying conductor condition and estimating the remaining useful

life of conductor; and

3. Identification of state-of-the-art commercially available conductor monitoring system and

emerging smart sensor-based system and possibly evaluating a smart sensor with data

collection methods.

The June 20th 2018, the project was formally started on June 20, 2018. Milestone 1 tasks were

completed in December 2018 and Milestone 1 report was submitted to ENA on December 20,

2018. After that, the UQ team is working on the tasks of Milestone 2. This report is the project

report for the period from January 1 to April 30, 2019.

1.2 Highlight of Project Progress

Having been working closely with project industry partners and two industry experts Colin Lee

and Keith Callaghan, the UQ team made significant progress.

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During the period of Milestone 1, the UQ team reviewed conductor population in Australian

Distribution Network Service Providers (DNSPs) circuits including the types, failure modes

and geographical locales of conductors [2]. The UQ team performed a comprehensive study to

understand the conductor degradation mechanism and parameters that affect each type of

degradation mechanism. Moreover, the UQ team reviewed the current Australian DNSP’s

practice on conductor asset management and their requirements for a proactive yet cost-

effective conductor condition monitoring. The UQ team identified core areas of research and

development for an improved condition assessment of conductors in Australian DNSPs’

circuits.

During the period from January 1 to April 30 2019, the UQ team has conducted a feasibility

study on the application of health index method to improve the condition assessment of bare

overhead conductors in Australian distribution networks. The UQ team did a comprehensive

review on two representative health index methods, i.e. Canadian method and EA technology

method. Moreover, the UQ team developed a model, which was based on the dataset of

conductor failure due to corrosion. The model can estimate the critical age, at which ACSR

conductors in different geographical zones can fail due to corrosion. Furthermore, the UQ team

developed a tool to estimate the remaining life of overhead conductors. The tool used a set of

indices to determine the conductors’ age de-rating factors, which includes conductor operating

conditions, geographical locations, frequencies of fault occurrence, the number of mid span

joints and the repair history.

Though some Australian DNSPs have been using commercial software tools for condition

based asset remaining life estimation, several issues still exist impairing the applicability of

these tools. These issues are: (1) what parameters are the most suitable inputs to the tools; (2)

how to determine the weights assigned to these parameters; and (3) how to obtain these

parameters from in-service circuits. The UQ is currently working on these issues and findings

will be included in Milestone 2 report.

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2. Industry and University Partners – Current

2.1 University team:

Tapan Saha

Hui Ma

Lakshitha Naranpanawe

University Lead: Tapan Saha

2.2 Industry:

Srinivansan Chinnargan (Energy Queensland),

Matthew Cupples (AusGrid),

Ayan Ghosal (Western Power),

Peter Livingston (CitiPower, Powercor, United Energy),

Tony Stevens (SA Power)

Industry Lead: Ayan Ghosal

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3. Project Progress / Methodology

In the past four months (from January 1, 2019 to April 30, 2019), the UQ team has been

working on Milestone 2, which objective is to investigate a methodology for quantifying

conductor condition and estimating the remaining useful life of conductor. The key research

and developed activities are:

1) Feasibility of developing condition assessment of conductor using health index

methodology. Investigation of globally documented conductor ‘health index’ methods

including the Canadian method (the health index is a weighted sum of conductor condition

parameters, field inspection data, and service records, used to estimate the remaining useful

life of conductor). Compare alternative Health index methods, assess suitability for

Australian conditions and select the best to be trialled in the project.

2) A demonstration of health index method for condition assessment of a representative type

conductor in Australia DNSPs’ lines. Demonstration of the selected ‘health index’ method

on a representative type of distribution conductor for the participating ENA members, and

benchmark the results against past investigation of conductor samples and metallurgical

studies (such as those undertaken by Western Power and other ENA members). Trialling

the Health index could be only possible provided the ENA member utility provides in-kind

contribution for testing, validation, and access of previous measurements on selective

conductor types.

This chapter of the report presents the methodologies, approaches, outcomes and discussions

of the above research and developed activities. The UQ team has conducted a comprehensive

review on two representative health index methods (Canadian method and EA technology

method), which is detailed in Sections 3.1 and 3.2. With the help from industry experts Colin

Lee and Keith Callaghan, the UQ team developed a method to estimate the remaining life of

overhead conductors. This method is presented in Section 3.3. Further discussions on the

advantages and limitations of the above three methods are provided in Section 3.4. To calculate

conductor health index and estimate conductor remaining life, the input parameters and their

weightages need to be properly defined. This can be achieved through statistical analysis of

conductor failure data and condition monitoring, which are presented in Section 3.5. The UQ

team has analysed conductor failure datasets from a number of Australian DNSPs. A summary

of these datasets is presented in Section 3.6. By analysing the datasets of conductor failure due

to corrosion, the UQ team has developed a methodology to extract conductor life information

and estimate the critical age, at which conductors in different geographical zones can fail due

to corrosion. The methodology is presented in Section 3.7.

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3.1 A Brief Review of Canadian Methodology of Conductor Health Index Calculation

The conductor health index (HI) methodology presented in this section was developed by a

team of Canadian researchers [3]. The health index was calculated using a set of condition

parameters, which are related to the long-term degradation factors leading to the conductor’s

end of life. Firstly, a set of condition parameter scores (CPS) are calculated as

1

.max

1

( )

( )

n

n n n

n

n

n n n

n

SCPS WSPC

CPS

SCPS WSPC

(1.1)

Where,

SCPS - Sub-Condition Parameter Score

WSCP - Weight of Sub-Condition Parameter

SCPSmax - Maximum Score for Sub-Condition Parameter

βn - Data availability coefficient

DRF - De-Rating Factor

Then, the above CPSs are used to calculate the health index as

1

.max

1

( )

( )

m

m m m

m

m

m m m

m

CPS WPC

HI DRF

CPS WPC

(1.2)

Where,

CPS - Condition Parameter Score

WCP - Weight of Condition Parameter

CPSmax - Maximum Score for Condition Parameter

αm - Data availability coefficient

In the Canadian method, the conductor’s mechanical strength (torsional ductility and tension)

was adopted as a critical parameter. Visual observations, service records, number of

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repairs/splices and the remaining tensile strength of the conductor samples were all used in

health index calculation.

3.2 A Brief Review of EA Technology’s CBRM Methodology of Conductor Health

Index Calculation

EA Technology’s Condition based risk management (CBRM 2.0) platform can calculate

assets’ health indexes and the probability of failure [4-6]. The main reasons for investigating

the EA technology’s CBRM method are: (1) the publically availability of the detailed

information of the EA Technology’s CBRM methodology, procedures and implementations;

and (2) Australian utility companies’ experience in using the EA technology’s CBRM tools

The EA Technology’s CBRM software is designed for the assessment, forecasting and

reporting of Asset Risk. Its main objectives are:

Comparative analysis of network asset performance between Distribution Service

Providers over time.

Assessment of the licensee's performance against the Network Asset Secondary

Deliverables.

Communication of information affecting the Network Asset Secondary Deliverables

between the licensee, the Authority and, as appropriate, other interested parties in a

transparent manner.

The key outputs of the EA Technology’s CBRM methodology is

An evaluation of Probability of Failure (PoF), which is the likelihood of condition-

based failure per annum for individual assets.

An evaluation of the Cost of Failure (CoF) associated with condition-based failures for

individual assets.

To calculate the PoF and CoF and eventually to obtain the risk matrix, the following input

parameters are used (refer to Figure 1):

Location Factors - factors relating to aspects of the environment, in which the asset is

installed and may have impact on the asset’s expected life.

Duty Factors – factors relating to the usage of the asset at its specific location that may

have impact on the asset’s expected life.

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Observed Condition Inputs - factors relating to the observed condition of the asset.

Measured Condition Inputs – factors relating to the condition/health of the asset

determined by measurements, tests or functional checks.

Reliability Modifier - a factor relating to generic reliability issues associated with the

individual make and type of an asset.

Figure 1 Methodology for calculating the probability of failure [2]

The factors such as Location Factor, Duty Factor, Health Score Modifier and Reliability

modifier in Figure 1 are calculated using the methodologies as summarized in Table 1.

Limitation of this method is described in section 3.4.

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Table 1 Parameters used to calculate PoF and CoF modifiers

Modifier Required Parameters

Location Factor Distance from the coast

Altitude

Corrosion category

Indoor/Outdoor

Duty Factor Asset category

Factors that introduce additional ageing due to the way the

asset is being used

Health Score Modifier Observed condition modifier (defined as follows)

Measured condition modifier (defined as follows)

Observed Condition Modifier Condition inputs from observations

Measured Condition Modifier Condition inputs from measurement

Reliability Modifier Based on the industry experience and available conductor

condition data

3.3 A Brief Introduction of the UQ Team’s Methodology of Estimating Remaining

Life of Conductors

The UQ teams’ conductor remaining life estimation methodology uses a set of input parameters

including

1. Conductor data (Type, Age, Climate zone and Conductor operating conditions)

2. Conductor operating conditions are

a. Whether the conductor is a distribution conductor

b. Whether the conductor is operating in a costal location

c. Does the conductor has high fault history

d. Level of tension in the conductor (high or low)

e. Whether the conductor span is above 300 m or not

f. Whether the conductor already has multiple repairs and mid span joints

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Each of the above parameters is assigned a de-rating factor. The de-rating factors are used to

compute the remaining life of the conductors. The UQ team’s software tool is implemented in

Microsoft Excel. Several screen shots of the current version of the UQ team’s software are

shown below.

Figure 2 The GUI of the UQ team developed software: Typical conductor life

Figure 3 The calculated results of the UQ team developed software: Conductor data input and estimated

data

It should be noted that UQ team is currently working with industry experts towards refining

the de-rating factors correspond to each conductor operating condition for improving the

performance of the above software tool.

3.4 Further Discussions on the Canadian and EA Technology Methods of Calculating

Conductor Health Index

According the existing literature, the Canadian health index methodology has shown promising

results [3]. However, after a feasibility study the UQ team found that,

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The Canadian method was developed mainly for transmission networks, on which

various sensors can be installed.

Canadian climate conditions are significantly different from that of Australia.

The detailed information of the input parameters used in the Canadian method is not

disclosed.

Based on the above facts, the UQ team has concluded that the Canadian method may not be

suitable to the Australian distribution network.

The EA Technology’s CBRM method provides a well-defined platform that can be used to

calculate the health indices of various power system assets. However, the standard CBRM

method is not tuned to produce an accurate health index of distribution overhead conductors.

For example, the standard CBRM method only uses visual condition and Mid-span joints as

the input parameter to derive the observed condition modifier of conductors (please refer to

Figure 1). However, according to the existing literature and experts’ knowledge, only visual

condition and Mid-span joints alone do not reflect the true condition of the conductor.

Therefore, one can suggest that the CBRM method needs to be refined by incorporating more

observed conditions and test data for accurately calculating the health indices of distribution

conductors. A number of Australian DNSPs have already used the EA Technology’s CBRM

methodology to determine health indices of their conductors. Hence, there is a timely

requirement of focusing our research on selecting the most suitable input parameters and

determining their weightages, which can be used as inputs of the EA Technology’s CBRM

method.

3.5 Input Parameters Proposed as Inputs for Calculating Conductor Health Index

To accurately calculate the health index for conductors in Australian distribution networks, it

is necessary to select a set of parameters that can accurately reflect the true condition of the

conductors under investigation. However, according to the UQ teams findings, the information

on how to select these parameters are still insufficient. The UQ team has started working on

identifying: (1) what parameters are the most suitable as the inputs to the tools; (2) how to

determine the weights to these parameters; and (3) how to obtain these parameters from in-

service circuits.

The input parameters the UQ team has identified so far are:

1. Maximum conductor operating temperature

2. Loss of cross section from lightning and fault current

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3. Loss of cross section due to conductor clashing (phase to phase faults and high winds)

4. Conductor location (i.e. the distance to the coast)

5. Loss of cross section from corrosion

6. Loss of galvanizing layer (ACSR and SC/GZ conductors)

7. Loss of cross section from galvanic corrosion (ACSR conductors)

8. Line has long spans in open area (greater than 200 m)

9. Loss of cross section from fatigue (at clamps and damper positions)

10. Loss of cross section due to vegetation impact

11. Number of repair sleeves

12. Condition of repair sleeves and adjacent conductor

13. Visual inspections records

Among the above input parameters, only items 11, 12 and 13 are considered in the EA

Technology’s CBRM methodology. The UQ team is currently working on refining the above

list and identifying the weighting factors for each of these parameters.

As the first step of the parameter identification process, the UQ team has started analysing the

conductor failure data collected from a number of Australian DNSPs.

3.6 Conductor Failure Data of Australian Distribution Networks

The conductor failure data received from Australian DNSPs are listed in Table 2. From the

table, it can be seen that all the data consist of conductor population, cause of failure and

voltage level. Except for the data provided by SA Power Networks, all data comprise sufficient

information for linking the cause of failure to the type of the conductor.

One vital information required for the statistical analysis of conductor failure data is the

conductor age when fail. Such information are available only in the data provided by Western

Power and Citipower/Powercor. The UQ team is conducting a Weibull analysis on conductor

failure data provided by Western Power.

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Table 2 Information of conductor failure data received form Australian DNSPs

Data source Content of the failure data received

Energy Queensland Conductor type

Cause of failure

Voltage level

Maintenance crews’ notes

Citipower/Powercor Conductor population/ Year Installed/ Climate Zone

Conductor failure data

o Conductor type

o Cause of failure

o Voltage level

o Conductor age when fail

Western Power Conductor age profile

Conductor failure data

o Conductor type

o Cause of failure

o Voltage level

o Conductor age when fail

o Climate Zone

SA Power

Networks

Conductor population

Cause of failure/ Voltage level – Conductor type unknown

Ausgrid Conductor Age Profile

Failure cause, Number of failures per year (2014 – 2018)

TasNetworks Conductor population

Distance from coastline

Conductor age profile

Length of conductors installed per year (1952 - 2014)

Age based conductor replacement cost

Conductor failure data (# of failures per year 2006 – 2017)

Conductor failure data

o Conductor type

o Cause of failure

o Voltage level

Maintenance crews’ notes

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3.7 Weibull Analysis for Extracting Conductor Life Information Data

Weibull analysis (also called "life data analysis") is a statistical method used to make

predictions on the life of assets in the population [7, 8]. This is achieved by fitting a statistical

distribution to data samples. The UQ team has conducting Weibull analysis on a data set

provided by Western power. The data set comprises of conductor type, climate zone, cause of

failure and conductor age at the time of failure.

Figure 4 and 5 illustrate the Weibull plots and conductor age distribution Steel, ACSR and

Aluminium conductors in various climate zones of Western Power distribution network

respectively.

Figure 4 Weibull plots correspond to Steel, Aluminium and ACSR conductors failed due to corrosion in

Western Power DN

Figure 5 Conductor age distribution in Western Power DN

y = 7.3406x - 27.947

y = 7.9065x - 30.387

y = 2.3307x - 8.1675

y = 5.8923x - 23.823

-5

-4

-3

-2

-1

0

1

2

2 2.5 3 3.5 4 4.5

ln(l

n(1

/(1

-Pf)

))

ln(age)

Steel-Arid Steel-costal Aluminium ACSR

0

200

400

600

800

1000

1200

1400

1600

1800

1 4 8 12 15 19 22 25 28 30 34 37 41 44 47 50 53 57 60 63 66 70 76

length

(km

)

Age( Years)

ACSR coastal Steel Arid Steel coastal Aluminium coastal

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The Weibull plots in Figures 4 used to calculate two statistical parameters, namely β and η. β

corresponds to the shape of the Weibull plot while η is a scale parameter representing the

characteristic life of the conductor. Values of these two parameters corresponding to Western

Power data are calculated and listed in Table 3.

Table 3 Statistical parameters calculated using Weibull modelling

β Characteristic life

η (years)

Mean time to

failure (MTTF)

(years)

Steel - Arid 7.3406 45 42

Steel - Costal 7.9065 45 43

Aluminium 2.3307 33 29

ACSR 5.8923 57 52

From Figure 4, Figure 5 and the values of β and η, it can be concluded that:

Steel-Arid: The length of steel conductors in Arid is 17,897 km. According to the data,

the average conductor age is 31.3 years. However, according to the Weibull modelling,

the characteristic life1 of the steel conductors in arid is 45 years. Further, β is 7.341

hence the failure data represent a wear-out failure of steel conductor due to corrosion.

As β = 7.341, the mean time to failure2 is 42 years.

Steel-Coastal: The length of steel conductors in coastal is 15,076 km. According to the

data, average conductor age is 44 years. However, according to the Weibull modelling,

the characteristic life of steel conductors in coastal areas is 45 years. Further, the β is

7.9, which implies failure data represent wear-out failure of steel conductors in coastal

areas. However, the mean time to failure is 43 years.

Aluminium: The length of aluminium conductors in coastal is 16,113 km. According

to the data, the average conductor age is 38.48 years. According to Weibull analysis, β

is 2.3307 and characteristic life is 33 years. This implies that the failure data represent

a wear-out failure of aluminium conductor due to corrosion. The mean time to failure

is 29 years.

1 Characteristic life (η)- the time at which 63.2% of the units will fail

2– Mean time to failure describes the expected time to failure for a non-repairable system

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ACSR: The length of ACSR conductors is 3,156 km. According to the data, the average

conductor age is 40 years. The β is 5.8923 and the characteristic life is 57 years. This

implies that the failure data represent a wear-out failure of steel conductors. The mean

time to failure is 52 years.

Based on above results, it can be seen that there is a difference between the conductor end-of-

life estimated using simple statistical parameters (i.e. mean value) and that obtained from the

Weibull analysis. Further, the Weibull analysis results are in a good agreement with the

conductor end of life figures typically use by Australian DNSPs. Therefore, the Weibull

analysis can be used to analyse conductor failure data. However, it should be noted that,

conductor failure data collected from DNSPs may not always contain the information required

to conduct a Weibull analysis.

4. Future Works

To conduct a detailed investigation of the parameters that Australian DNSPs should use

as the input parameters to their in-house condition-based asset management tools.

To investigate the methods of obtaining, calculating and estimating the above input

parameters.

To identify the weighting factors for each of the input parameters.

To incorporate the refined input parameters with suitable weighting coefficients and

provide to one of project partners to use in their in-house condition-based asset

management tool on a selected type of conductors (given that the UQ team is granted

the access to the tool by the project partner).

A feasibility study of using drone to take high-resolution photos of conductors and then

evaluate the conductor condition from these photos. This is the part of the first task in

Milestone 3 of the project proposal.

5. Conclusions and Recommendations

The following conclusions and recommendations are made:

The Canadian method was developed for transmission networks, on which various

sensors can be installed. However, Canadian climate conditions are significantly

different from that of Australia. Moreover, the detailed information of the input

parameters used in the Canadian method are not disclosed. As a result, the Canadian

method may not be suitable to the Australian distribution network.

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Some of Australian DNSPs in-house asset management software tools can provide

health index calculation and predict the probability of failure of overhead conductors

in distribution networks. However, there is still lack of understanding of input

parameter selection. Hence, the accuracy of these software tool cannot be guaranteed.

To deal with such difficulties, the UQ team has been investigating the input parameters,

which can reflect the condition of the conductors more accurately.

The UQ team found that the conductor failure data collected from Australian DNSPs

do not always contain sufficient information, which may impair the accuracy of

calculating conductors’ health index and estimating conductors’ remaining useful life.

The UQ team is currently working towards improving the accuracy of existing

conductor health index calculation techniques.

6. References

[1] R. K. Aggarwal, A. T. Johns, J. A. S. B. Jayasinghe, and W. Su, "An overview of the

condition monitoring of overhead lines," Electric Power Systems Research, vol. 53, no.

1, pp. 15-22, 2000/01/05/ 2000.

[2] H. M. Lakshitha Naranpanawe, Tapan Saha, "Overhead Conductor Condition

Monitoring : Milestone REport 1," December 2018.

[3] Y. Tsimberg, K. Lotho, C. Dimnik, N. Wrathall, and A. Mogilevsky, "Determining

transmission line conductor condition and remaining life," in 2014 IEEE PES T&D

Conference and Exposition, 2014, pp. 1-5.

[4] DNO COMMON NETWORK ASSET INDICES METHODOLOGY. Available:

https://www.ofgem.gov.uk/system/files/docs/2017/05/dno_common_network_asset_i

ndices_methodology_v1.1.pdf

[5] E. Technology. Condition Based Risk Management (CBRM). Available:

https://www.eatechnology.com/wp-content/uploads/2017/03/CBRM-brochure.pdf

[6] E. Technology. CBRM 2.0. Available: http://www.eatechnology.com.cn/wp-

content/uploads/pdfs/10348_EA_CBRM_2_Brochure_LR_V4.pdf

[7] R. B. Abernethy, J. Breneman, C. Medlin, and G. L. Reinman, "Weibull analysis

handbook," Pratt and Whitney West Palm beach fl Government Products DIV1983.

[8] W. Nelson, "Weibull analysis of reliability data with few or no failures," Journal of

Quality Technology, vol. 17, no. 3, pp. 140-146, 1985.

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7. Appendix

7.1 Risks

Insufficient information contained in the conductor failure data provided by the industry

partners.

Inaccuracy of some type of data provided by the industry partners.

The UQ team is not given the access to the industry partners in –house asset management

tools.

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7.2 Gantt Chart/ Project Plan

Task

Time (months)

1-6 7-10 11-12 13-14 15-18

Milestone 1

Review, literature study, analysis and

writing report

Milestone 2

Collecting conductor failure data

modelling and analysing

Determining feasibility of developing

condition assessment of conductor

using health index methodology

Study of DNSPs in-house assert

management software tools

Identifying input parameters

Identifying weighting factors for

the input parameters

A demonstration of health index

method for condition assessment of a

representative type conductor in

Australia DNSPs’ lines

Milestone 3

Survey state-of-the-art conductor

condition monitoring techniques

Evaluating prospective emerging smart

sensor-based conductor monitoring

systems and provide some

recommendations

- Completed tasks

- In-Progress Tasks

24

7.3 Financial statement

2018

Consolidated Carry

Fwd

Aug - Dec Adjustments YTD

Actuals

Carry

Forward

Jan

Feb Mar Apr Adjustments YTD

Actuals

Revenue

External Revenue

Research Income 0.00 0.00 0.00 0.00 0.00 90,000.00 0.00 0.00 0.00 0.00 90,000.00

Total External Revenue 0.00 0.00 0.00 0.00 0.00 90,000.00 0.00 0.00 0.00 0.00 90,000.00

Internal Transfers

UQ Wide Transfers

Expense

0.00 0.00 0.00 0.00 0.00 (13,163.56) 0.00 0.00 0.00 0.00 (13,163.56)

Total Internal Transfers 0.00 0.00 0.00 0.00 0.00 (13,163.56) 0.00 0.00 0.00 0.00 (13,163.56)

TOTAL REVENUE 0.00 0.00 0.00 0.00 0.00 76,836.44 0.00 0.00 0.00 0.00 76,836.44

Expenditure

Academic Salaries

Salaries - Academic Non

Casual

0.00 47,026.59 0.00 47,026.59 0.00 9,299.73 8,780.37 9,223.64 9,053.24 0.00 36,356.98

Total Academic Salaries 0.00 47,026.59 0.00 47,026.59 0.00 9,299.73 8,780.37 9,223.64 9,053.24 0.00 36,356.98

General Salaries

Salaries - General Casual 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Total General Salaries 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Other Expenditure

General Operating

Expenses

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Consultant Professional &

Oth

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Travel 0.00 0.00 0.00 0.00 0.00 32.28 0.00 0.00 0.00 0.00 32.28

Total Other Expenditure 0.00 0.00 0.00 0.00 0.00 32.28 0.00 0.00 0.00 0.00 32.28

TOTAL EXPENDITURE 0.00 47,026.59 0.00 47,026.59 0.00 9,332.01 8,780.37 9,223.64 9,053.24 0.00 36,389.26

Operating

Surplus/(Deficit)

0.00 (47,026.59) 0.00 (47,026.59) 0.00 67,504.43 (8,780.37) (9,223.64) (9,053.24) 0.00 40,447.18

Carry Forward 0.00 0.00 0.00 (47,026.59) 0.00 0.00 0.00 0.00 0.00 (47,026.59)

Accumulated Position (47,026.59) (6,579.41)

* Currently the project is in deficit by $6,579.41.

25